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TruthfulQA

Papers

Showing 125 of 80 papers

TitleStatusHype
RLHF Workflow: From Reward Modeling to Online RLHFCode5
DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language ModelsCode2
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination MitigationCode2
Inference-Time Intervention: Eliciting Truthful Answers from a Language ModelCode2
Tuning Language Models by ProxyCode2
TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful SpaceCode2
Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and EthicsCode1
Non-Linear Inference Time Intervention: Improving LLM TruthfulnessCode1
Integrative Decoding: Improve Factuality via Implicit Self-consistencyCode1
Instruction Tuning With Loss Over InstructionsCode1
TruthfulQA: Measuring How Models Mimic Human FalsehoodsCode1
Red-Teaming Large Language Models using Chain of Utterances for Safety-AlignmentCode1
Alleviating Hallucinations of Large Language Models through Induced HallucinationsCode1
Tool-Augmented Reward ModelingCode1
Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without TuningCode1
Machine Unlearning in Large Language ModelsCode1
RAIN: Your Language Models Can Align Themselves without FinetuningCode1
DeLTa: A Decoding Strategy based on Logit Trajectory Prediction Improves Factuality and Reasoning AbilityCode0
Enhancing Language Model Factuality via Activation-Based Confidence Calibration and Guided DecodingCode0
SaGE: Evaluating Moral Consistency in Large Language ModelsCode0
Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human FeedbackCode0
A test suite of prompt injection attacks for LLM-based machine translationCode0
Multi-Agent Reinforcement Learning with Focal Diversity OptimizationCode0
Instruction Tuning with Human CurriculumCode0
CHAIR -- Classifier of Hallucination as ImproverCode0
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